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dc.contributor.authorGan, Xiangchaoen_US
dc.contributor.authorLiew, Alan Wee-Chungen_US
dc.contributor.authorYan, Hongen_US
dc.contributor.editorDaniel S. Yeung, Zhi-Qiang Liu, Xi-Zhao Wang, Hong Yanen_US
dc.date.accessioned2017-04-04T21:28:28Z
dc.date.available2017-04-04T21:28:28Z
dc.date.issued2005en_US
dc.date.modified2010-10-13T10:00:34Z
dc.identifier.doi10.1109/ICMLC.2005.1527527en_AU
dc.identifier.urihttp://hdl.handle.net/10072/26081
dc.identifier.urihttp://www4.comp.polyu.edu.hk/~cike/icmlc2005/home.htmen_AU
dc.description.abstractIn gene expression data, a bicluster is a subset of genes exhibiting a consistent pattern over a subset of the conditions. In this paper, we propose a new method to detect biclusters in gene expression data. Our approach is based on the high dimensional geometric property of biclusters and it avoids dependence on specific patterns, which degrade many available biclustering algorithms. Furthermore, we illustrate that a bilclustering algorithm can be decomposed into two independent steps and this not only helps to build up a hierarchical structure but also provides a coarse-to-fine mechanism and overcome the effect of the inherent noise in gene expression data. The simulated experiments demonstrate that our algorithm is very promising.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent469407 bytes
dc.format.extent25242 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherIEEEen_US
dc.publisher.placeUSAen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencenameFourth International Conference on Machine Learning and Cyberneticsen_US
dc.relation.ispartofconferencetitleProceedings of the Fourth International Conference on Machine Learning and Cyberneticsen_US
dc.relation.ispartofdatefrom2005-08-19en_US
dc.relation.ispartofdateto2005-08-21en_US
dc.relation.ispartoflocationChinaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode280207en_US
dc.subject.fieldofresearchcode270201en_US
dc.titleBiclustering Gene Expression Data based on a High Dimensional Geometric Methoden_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.rights.copyrightCopyright 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_AU
gro.date.issued2005
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